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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Selection of Training Sets for 235U Source Detection Classifiers Using Gamma Signatures

The machine learning methods for classifiers to detect low level radiation sources are of interest when suitable training data sets are available. Their application and performance assessment, however, involves the aspects of over-fitting and training data selection that are somewhat uncommon in other existing methods for this task.We study U-235 gamma signatures using data sets collected by 21 NaI detectors under controlled conditions. The gamma spectra are collected by the detectors located at different distances from the source, and we study their choice as training sets for classifiers to detect a source. The detectors form the near, middle and outer groups based on the distance to source. The classifiers based on the outer group are susceptible to over-fitting, that is, they achieve low training error but incur much higher testing error in independent tests. The other two groups achieve lower training error and comparable testing error, and the near group achieves the overall lowest error. In detecting a source at an unknown distance, the farther detectors in the middle group achieve the overall lowest testing error with limited over-fitting, thereby indicating the complex dependencies between the training and classifier performance.

Rao, Nageswara↗
DOE OSTI2023

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. However, it is not completely accurate because the phase to phase ground and phase to ground electrical faults could have the same detection conditions. Therefore, engineers need to plot the events after an electrical fault to observe the nature of the incidents in detail. In this study, the phase to ground fault apparent (PGFA) admittance method with phase/ground boundaries identified the types of electrical faults located in distribution power lines and feeders. This method was based on measuring the PGFA admittance magnitudes for the faulted and non-faulted phases, resulting in greater than zero and near zero, respectively. The PGFA admittance algorithm was built with MATLAB/ Simulink software and tested with signature library and grid simulation events. The PGFA method with phase/ground boundaries was evaluated with the confusion matrix. The measured and predicted values matched in more than 90% of the tests, and the PGFA admittance method with phase/ground boundaries presented an accuracy of 94.3% and a precision of 100%.

Piesciorovsky, Emilio↗
DOE OSTI2024

Kick Signatures Through Advanced Multi-Phase Data

Existing downhole geophysical sensors, like those used in LWD, offer an opportunity for early kick detection, but they are not currently used for detection or characterization. Downhole acoustic measurements also have the potential for providing reliable gas leak detection based on their high sensitivity to low concentrations of gas present in a gas-liquid mixture. This effort seeks to close a data knowledge gap through numerical simulation and use this data to aid in development of a low-cost, fast, and reliable early kick detection technology for preventing blowouts based on the acoustic signature.

Carney, Janine↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collectedunder structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore,their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distanceto the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensembleof trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fittingobserved in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inversephysical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of thesource strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations ofunderlying regression methods.

Rao, Nageswara↗
DOE OSTI2024

Physics-Based Machine Learning Methods for U-235 Forensics Signatures

Signatures of low-intensity U-235 sources have been recently studied by utilizing a variety of machine learning (ML) classifiers using features derived from gamma spectral measurements collected under structured campaigns. Several ML classifiers, such as ensemble of tress and classification trees, revealed misleadingly-optimistic training error due to over-fitting, and furthermore, their performance is not directly relatable to the physical properties due to their data-driven, opaque designs. We present a regression-based ML method that first estimates the inverse distance to the source and then utilizes a threshold to infer its presence, by representing the background as a source located at an infinite distance. For the inverse distance estimation, we study the ensemble of trees and Gaussian process regression methods, and a hyper parameter auto-tuning and selection method that employs five regression estimators. These methods avoid the over-fitting observed in several ML classifiers, while providing the classification error nearly comparable to them based on independent test data. Their error is directly related to estimates of the inverse physical distance to source, and the precision of error determines the seperability property that determines the false alarm and missed detection rates. The property of monotonic decrease of the source strength with increasing detector distance combined with Poisson distribution of measurements is utilized to analytically validate these methods by deriving the generalization equations of underlying regression methods.

Rao, Nageswara↗
DOE OSTI2025

Rare Lepton Decays and Differentiable Hadronization Models - From Signatures of New Physics to Data-driven Event Generation

This dissertation is partitioned into two parts: phenomenological studies focused on rare lepton decays as probes of heavy and light new physics, and the development of differentiable, data-driven hadronization models. Part I develops the phenomenology of new physics signatures stemming from rare charged lepton flavor violating decays probed by experiments at the intensity frontier. These include interactions mediated by both high-scale effective operators and light new physics, manifesting in multi-lepton final states ($\mu \to 5e$), elastic nuclear transitions ($\mu \to e$ conversion), baryon-number-violating muon capture, and time-dependent signals from ultralight dark matter ($\mu \to e \phi, \tau \to \ell \phi$). Part II develops two distinct strategies for advancing differentiable and data-driven hadronization models. One involves comprehensive reweighting frameworks for hadronization that enable efficient uncertainty estimation, facilitate parameter tuning, and interface naturally with differentiable programming paradigms. The other introduces machine-learning-based methods for extracting microscopic fragmentation dynamics directly from macroscopic observables through the deformation of existing models -- effectively providing solutions to the inverse problem of hadronization. Altogether, these studies advance the interpretability, flexibility, and precision of theoretical predictions for both high-intensity and high-energy experiments.

Menzo, Tony [Cincinnati U.] (ORCID:000000022013457↗
DOE OSTI2020

Spectroscopic signatures of next-nearest-neighbor hopping in the charge and spin dynamics of doped one-dimensional antiferromagnets

In this work, we study the impact of next-nearest-neighbor (nnn) hopping on the low-energy collective excitations of strongly correlated doped antiferromagnetic cuprate spin chains. Specifically, we use exact diagonalization and the density matrix renormalization group method to study the single-particle spectral function, the dynamical spin and charge structure factors, and the Cu L-edge resonant inelastic x-ray scattering (RIXS) intensity of the doped t-t'-J model for a set of t' values. We find evidence for the breakdown of spin-charge separation as |t'| increases and identify its fingerprints in the dynamical response functions. The inclusion of nnn hopping couples the spinon and holon excitations, resulting in the formation of a spin-polaron, where a ferromagnetic spin polarization cloud dresses the doped carrier. The spin-polaron manifests itself as additional spectral weight in the dynamical correlation functions, which appear simultaneously in the spin- and charge-sensitive channels. We also demonstrate that RIXS can provide a unique view of the spin-polaron, due to its sensitivity to both the spin and charge degrees of freedom.

1D spin chains↗